Swarm Intelligence Optimization Algorithm Research Articles

Frigatebird optimizer: a novel metaheuristic algorithm

Abstract This paper proposes a new swarm intelligence optimization algorithm - the Frigate Bird Optimizer (FBO). The algorithm is inspired by the unique flight and foraging behaviors of frigatebirds. Its optimization process is divided into two stages: The first stage simulates frigatebirds harassing other seabirds to snatch food, and the search direction and radius of individuals have randomness and uncertainty, which is conducive to global exploration; The second stage simulates frigatebirds observing large fish driving small fish to leap out of the water and preying on them, and individuals tend to gather in the optimal search direction. By simulating the behaviors of frigatebirds under different survival strategies, the algorithm achieves extensive global search in the first stage and fine-tuned local optimization by learning information in the second stage. To evaluate its performance, 46 functions in the CEC2014 and CEC2017 benchmark test sets are selected as objective functions and compared with 9 state-of-the-art meta-heuristic algorithms. The results show that the FBO algorithm has higher performance, excellent iterative optimization ability and strong robustness, and can be applied to different optimization domains.

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability

The use of nature-inspired meta-heuristics to tackle complex optimization problems is steadily gaining popularity within a rapidly evolving world. Swarm intelligence (SI) optimization motivated by behavior of community-based organisms of flocks of birds, schools of fish, colonies of ants and bees performs the search through agents whose trajectories are primarily adjusted stochastically and sporadically deterministically, by golden rules drawn from Mother Nature. Each entity within the swarm is swayed by its individual ‘best’ and group’s ‘best’ position while moving randomly to converge to optimal through competition and cooperation. The sparrow search algorithm (SSA), developed by Xuea and Shen (Xue and Shen in Syst Sci Cont Eng 8:22–34, 2020) is a very recent SI approach that adopts the sparrow producer–scrounger model metaphorically for designing optimum searching strategies, inspired by the foraging, anti-predation behavior, and the overall group wisdom of sparrows. SSA has been experimented on some hard benchmark test functions to test its effectiveness and thereafter, applied in slope-stability problems in searching the critical failure surface. The objective function to be optimized is the factor of safety against failure given by Bishop's (Bishop in Geotechnique 5:7–17, 1955) simplified method. Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. The study illustrates the flexibility, efficiency, and robustness of the methodology in function optimization.

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

A novel memory usage guided fuzzing based on particle swarm optimization

Fuzzing has become the focus of research in the field of software testing due to its advantages such as simple testing process, easy deployment, and easy reproduction of crashes. However, fuzzing also suffers from the disadvantages of poor test purpose and invalid generated seeds. To solve the above problems, researchers proposed the Memory Usage Guided Fuzzing (MUGF). To further optimize the performance of MUGF, this paper proposes a fuzzing method named Memory Usage Guided Fuzzing based on Particle Swarm Optimization (MUGF-PSO). MUGF-PSO will guide the selection of mutation operators for subsequent testing by learning the effectiveness of each mutation operators during previous testing. Specifically, MUGF-PSO regards each mutation operator as a particle in the particle swarm optimization algorithm, and the coverage change is regarded as an important factor to evaluate the seed’s local and global best position. In addition, the efficient selection probability distribution of mutation operator is constantly iteratively searched to make the MUGF tends to select the mutation operator that is more able to trigger new coverage. Furthermore, the MUGF-PSO is integrated into MemLock developed by MUGF, that is, MemLock-PSO is developed. We conduct a comparison experiment with 5 fuzzers (including AFL, MemLock, EcoFuzz, HavocMAB and Darwin), and the results show that MemLock-PSO is able to find more paths and crashes in 12 widely used program with different functions from 10 tools, with a significant gap of around 12 h. Meanwhile, the MemLock-PSO have a significant difference in the number of paths and crashes compared to other fuzzers with a good stability. Our work also proves the remarkable significance of applying swarm intelligence optimization algorithms in fuzzing in order to solve the problem of selection of mutation operators.

Swarm intelligent optimization algorithms for precision gene selection in microarray-based cancer classification

Swarm intelligent optimization algorithms for precision gene selection in microarray-based cancer classification

Rolling bearing fault diagnosis based on optimized A-BiLSTM

In order to improve the efficiency of hyperparameter setting and its adaptability to the model, and to reduce the high cost and low efficiency of manually setting model parameters, a rolling bearing fault diagnosis method based on Honey Badger Algorithm (HBA) optimized attention bidirectional long short-term memory network (HBA-A-BiLSTM) is proposed. Firstly, the optimal hyperparameter combination of A-BiLSTM model is searched by HBA, and then the fault diagnosis performance of A-BiLSTM model under the optimal hyperparameter is tested. Finally, the generalization ability of the model is tested based on data sets under different working conditions. The fault diagnosis effect of the proposed method is verified by using CWRU data set, and the diagnosis accuracy and confusion matrix are used for evaluation. The experimental results show that compared with other swarm intelligence optimization algorithms, the Honey Badger algorithm has good global search performance and fast convergence speed. The fault diagnosis accuracy of the optimized final model reaches 99.5%, which has good effects. It can also achieve stable and accurate fault diagnosis performance under different working conditions and has strong generalization ability.

Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable.

Optimizing Deep Learning Models with Improved BWO for TEC Prediction.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

Structural optimization design and compression performance of porous titanium prepared by laser powder bed fusion

Porous implants composed of periodically-repeating porous units have been applied in orthopedic surgeries to replace damaged bones. An important requirement for bone implants is to find a balance between biological and mechanical properties, that is, to ensure high pore connectivity and a certain strength. In this work, center-supported and perimeter-supported porous optimization models were established by a swarm intelligence optimization algorithm, and the corresponding titanium porous samples were fabricated by laser powder bed fusion (L-PBF). The effects of cell topology and porosity on the compressive behavior of L-PBF titanium porous structures were systematically investigated by a combination of compression simulation and experiments. Compression simulation shows that the deformation failure behavior of center-supported and perimeter-supported porous structures consists of three stages: elastic deformation, plastic deformation and compression collapse, showing the characteristics of elastic-plastic porous materials. According to the compression test results, the compressive strength of center-supported and perimeter-supported structure samples are 151–188 MPa and 178–196 MPa, respectively, and the compressive stability of the perimeter-supported structure is better than that of the center-supported structure. The porosity error of center-supported structure is 1.6%–2.9%, and the porosity error of perimeter-supported structure is 3.1%–4.5%. The compressive strength of the same type of structure decreases with the increase of the porosity of the porous structure. The simulation/experimental compressive strength errors of center-supported and perimeter-supported structures are less than 6% and 5.9%, respectively. The constructed quasi-static compression model has certain guiding significance for the study of the fracture of the titanium implants under external force.

Large-scale magnetic target state estimation using model parameter optimization

Abstract Aiming at the problem of non-negligible target scale in the state estimation of magnetic targets, a state estimation method that requires only the measurement data of a single three-axis magnetic sensor and is insensitive to the initial value is proposed. Firstly, the state estimation model of a single three-component magnetic sensor is established based on a magnetic dipole array. A signal preprocessing method is designed to reject useless signals and keep the waveform intact. Five swarm intelligent optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Hummingbird Algorithm (AHA), Artificial Rabbits Optimization (ARO), Grey Wolf Optimizer (GWO), and Improved GWO (IGWO) are introduced to perform state optimization, and two methods, such as Conjugate Gradient Least Squares (CGLS), and Stepwise Regression (SR), are introduced to calculate the magnetic moment parameters, and PSO-CGLS, AHA-CGLS, AHA-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, and ARO-CGLS are designed in combination. CGLS, ARO-CGLS, GWO-CGLS, IGWO-SR, IGWO-CGLS, and six state estimation methods. In order to address the problem that the parameter optimization range is too large, the state to be estimated is adjusted from the initial state to the state at the moment of the maximum value of the magnetic field modulus, and the method of positive transverse distance estimation is used to narrow the range of positive transverse position optimization. Finally, numerical simulation tests and ship model tests are used for verification, and the results show that the proposed methods are accurate and can meet the needs of precise target state estimation.

Research on the Capacity Allocation and Planning Model of a Flexible Direct Current Transmission System Based on Swarm Intelligence Optimization Algorithm

Based on the contradiction between the increasing power demand and the low capacity of the traditional direct current (DC) transmission system, this study innovatively combines the swarm intelligence optimization algorithm with power transmission and storage, and establishes a flexible DC transmission system model based on the swarm intelligence optimization algorithm. The study also analyzes the difference between the optimized capacity configuration of flexible DC transmission system and the traditional transmission system under different factors. The research results show that the transmission mode has become an important factor affecting the capacity allocation of flexible DC transmission system. The transmission medium and transmission cable type also have an impact on the capacity allocation of flexible DC transmission system. Optimizing power transmission mode, changing transmission medium and whether to add the swarm intelligence optimization algorithm are of far-reaching significance for improving the capacity allocation of flexible DC transmission system.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets.

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

Blockchain technology, based on decentralized data storage and distributed consensus design, has became a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), It optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.

A swarm intelligence optimization algorithm for human-robot collaborative energy-efficient shop scheduling

A swarm intelligence optimization algorithm for human-robot collaborative energy-efficient shop scheduling

An improved chaotic GWO-LGBM hybrid algorithm for emotion recognition

To address the challenges associated with the unbalanced search abilities and low search accuracy of the Gray Wolf Optimization (GWO) algorithm, we propose an enhanced version termed the Improved Chaotic Gray Wolf Optimization Algorithm (ICGWO) based on cubic mapping. Initially, the population positions of gray wolves were perturbed to expedite the convergence speed. Subsequently, a nonlinear convergence factor was introduced to enhance both search ability and efficiency. Finally, within the algorithm, a dynamic inertia-weighting strategy was implemented to improve convergence speed and solution accuracy. Experimental simulations encompassing 23 classical test functions demonstrate that the ICGWO algorithm exhibits superior convergence speed, solution accuracy, and stability compared to other swarm intelligence optimization algorithms and certain enhanced GWO algorithms. In comparison to GWO, utilizing the ICGWO method for hyperparameter optimization within the Light Gradient Boosting Machine (LGBM) for emotional recognition led to notable enhancements in average accuracy and F1 score by 7.29 % and 3.92 % respectively for valence, and 4.33 % and 3.23 % respectively for arousal. Additionally, the standard deviation of the ICGWO-LGBM was less than one-tenth of the standard deviation of GWO-LGBM in both conditions. These results underscore the strong optimization capabilities and high stability of the ICGWO algorithm.

A HEART FAILURE PREDICTION ALGORITHM BASED ON IMPROVED REINFORCEMENT LEARNING FRAMEWORK

Reducing the occurrence of diseases has become an important research direction in today’s social medicine. Accurate prediction of heart failure can greatly reduce the complication rate, allowing patients to know the condition in advance and receive treatment. To deal with heart failure, this paper proposes a heart failure prediction algorithm based on a reinforcement learning framework. This paper combines and improves the reinforcement learning algorithm and the swarm intelligence (SI) optimization algorithm, aiming to use the reinforcement learning algorithm to improve the global optimization capability of the SI optimization algorithm. In order to better improve the global search ability of the SI optimization algorithm, the penalty function will be replaced by a combination mode of dynamic and static reward. The improved reinforcement learning algorithm framework is significantly better than the previous algorithm framework and has been used to predict heart failure. The experimental results have proven the effectiveness of the algorithm and its accuracy in prediction of heart failure.

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.